| 选择性聚类融合研究进展 |
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| 引用本文： | 刘丽敏,樊晓平,廖志芳. 选择性聚类融合研究进展[J]. 计算机工程与应用, 2012, 48(10): 1-5,15 |
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| 作者姓名： | 刘丽敏 樊晓平 廖志芳 |
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| 作者单位： | 1. 中南大学信息科学与工程学院,长沙,410075
2. 中南大学软件学院,长沙,410075 |
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| 基金项目： | 国家自然科学基金（No.60774023）; 湖南省自然科学基金（No.06JJ50143） |
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| 摘 要： | 传统的聚类融合方法通常是将所有产生的聚类成员融合以获得最终的聚类结果。在监督学习中,选择分类融合方法会获得更好的结果,从选择分类融合中得到启示,在聚类融合中应用这种方法被定义为选择性聚类融合。对选择性聚类融合关键技术进行了综述,讨论了未来的研究方向。
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| 关 键 词： | 聚类融合 选择性聚类融合 选择策略 融合函数 |
Study on clustering ensemble selection |
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| LIU Limin , FAN Xiaoping , LIAO Zhifang. Study on clustering ensemble selection[J]. Computer Engineering and Applications, 2012, 48(10): 1-5,15 |
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| Authors: | LIU Limin FAN Xiaoping LIAO Zhifang |
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| Affiliation: | 1.School of Information Science and Engineering, Central South University, Changsha 410075, China 2.School of Software, Central South University, Changsha 410075, China |
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| Abstract: | Traditional clustering ensemble combines all of the available clustering partitions to get the final cluster-ing result. But in supervised classification area, it has been known that selective classifier ensembles can always achieve better solutions. Following the selective classifier ensembles, the question of clustering ensemble is defined as clustering ensemble selection. The paper introduces the concept of clustering ensemble selection, gives the survey of clustering ensemble selection algorithms and discusses the future directions of clustering ensemble selection. |
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| Keywords: | clustering ensemble clustering ensemble selection selection strategy consensus function |
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